Architecting Agentic AI Systems
To document an agentic AI system, cover these layers in order:
- Executive summary + architecture diagram — what makes it "agentic" vs. a chatbot (persistence, background execution, goal decomposition)
- Core engine specs — model/reasoning layer, orchestration/scaffolding layer, interoperability protocols
- Operational prerequisites — identity, licensing, compliance/geofencing, hard system limits
- Integration surface — first-party app hooks, third-party protocol bridges, workflows per integration
- Configuration primitives — Task / Schedule / Skill (or equivalent) with a comparison table
- Security & governance — sandboxing, confirmation boundaries, data retention, downgrade lifecycle
Use ASCII architecture diagrams for layered systems and Markdown tables for primitive comparisons — these compress dense information for engineering readers.
Progress:
- Step 1: Establish the core distinction (agentic vs. conversational) in one paragraph
- Step 2: Draw the layered architecture diagram (UI → Compute → Intelligence/Orchestration → Integration Subsystems)
- Step 3: Document the intelligence engine (context window, output limits, internal reasoning/refinement passes, speed benchmarks)
- Step 4: Document the orchestration layer (sub-agent concurrency, execution duration, memory/session handling)
- Step 5: Document interoperability protocols (open standards for tools, payment/commerce protocols, their guardrails)
- Step 6: Specify operational prerequisites — identity/age, licensing tiers with prices, telemetry dependencies
- Step 7: Specify compliance boundaries — excluded regions/jurisdictions, sub-regional restrictions, rationale
- Step 8: Specify hard system limits — concurrency ceilings, rate limits, failure behavior
- Step 9: Document each first-party integration as its own subsection with a mini pipeline diagram and numbered/bulleted capabilities
- Step 10: Define the configuration primitives table (Task/Schedule/Skill or equivalent) — definition, syntax, lifecycle
- Step 11: Provide a reference SKILL.md-style implementation showing objectives, tool mappings, conditional logic, fallback routes, safety constraints
- Step 12: Write the security section — sandbox isolation, confirmation boundary (passive vs. active action classes), retention/downgrade lifecycle
- Step 13: Close with a customization prompt — ask what integrations, throughput needs, and compliance requirements the reader wants elaborated
Example 1: Input: "Document the security model for an AI agent that can send emails and make payments autonomously." Output: A two-tier model — Passive Background Space (silent execution: reading, categorizing, internal analysis) vs. Active Verification Space (execution halts, requires explicit human approval for: contacting new parties, public sharing, deletions, financial transactions). Explicitly state the agent "refuses to progress until a human cryptographically signs or approves."
Example 2:
Input: "Define the Skill primitive for a multi-agent orchestration system."
Output: A reusable Markdown instruction file (SKILL.md) with four required sections: Core Objectives (numbered goals), Architectural Tool Mappings (bulleted API/protocol list), Implementation Rules and Logical Pathing (numbered steps with explicit if/then fallback branches), Safety and Boundary Constraints (CRITICAL-flagged hard rules, spending ceilings). State that it's "automatically injected into the system prompt space when relevant tool logic is invoked."
Example 3: Input: "What compliance boundaries should an autonomous background-agent product document?" Output: List excluded jurisdictions (e.g., EEA, UK, Switzerland — regions with strict automated-decision-making law), note any single excluded non-obvious country to signal real regulatory research, and separately call out feature-level geofencing (e.g., biometric/media features restricted below the country level to specific states) as distinct from full-product exclusion.
- Separate the reasoning engine from the orchestration layer as distinct named components — this mirrors how real systems (model vs. scaffolding) are built and makes the doc credible.
- Quantify everything: context window sizes, concurrency ceilings, cache/retention windows in hours, pricing tiers in dollars, speed multipliers. Vague claims ("fast," "scalable") read as filler.
- Always pair a capability with its failure mode. Every automation feature needs a stated fallback (halt, log, escalate to human) — this is what distinguishes production-grade documentation from marketing copy.
- Use the passive/active action dichotomy as the default security framework for any system with irreversible external side effects (sending, sharing, deleting, paying).
- Give ephemeral state explicit lifetimes. Temporary files, sandboxed sessions, and scratchpads should always have a stated expiry (e.g., "24 hours or until next root query").
- End integration sections with a pipeline diagram showing data flow left-to-right or top-to-bottom through 3-5 stages — this is more scannable than prose for engineers.
- Table format for primitive comparisons (Task/Schedule/Skill-style): columns should be Definition, Syntax/Implementation, Lifecycle — this triad covers what/how/when for any operational primitive.
- Don't describe capabilities without concurrency/rate limits — every "autonomous" or "background" system needs a stated ceiling and defined failure behavior when exceeded.
- Don't omit the human-in-the-loop boundary — any autonomous system touching money, external communication, or deletions must specify exactly which actions require explicit confirmation vs. which run silently.
- Don't leave compliance sections generic ("various regions") — name specific jurisdictions and the legal rationale category (e.g., automated decision-making law, biometric privacy statutes).
- Don't present Skills/instruction-files as freeform prose — they need enforced structure (objectives, tool mappings, logic, safety constraints) to be reusable and auditable.
- Don't forget the downgrade/offboarding lifecycle — what happens to in-flight tasks, stored configs, and scheduled triggers when a user loses access tier is as important as the happy path.